1. Field
The following description relates to ontology matching of large-scale biomedical ontologies, more particularly, parallel matching and distribution process of the ontology matching.
2. Description of the Related Art
In recent years, as semantic web tools are applied to biomedical field, many advantages are brought in return. In particular, ontologies in the biomedical systems are frequently used for standardization of biomedical information, knowledge sharing, and reusability. As a result, ontologies are applied to fields like Gene Ontology (GO), National Cancer Institute Thesaurus (NCI), Foundation Model of Anatomy (FMA), and Systemized Nomenclature of Medicine Clinical Terms (SNOMED CT). As described above, various researches effectively apply the ontology to the biomedical field, and researches for providing the already developed ontology with the durability are also made.
The ontologies in the biomedical field are very complicated and large-scale because of the ever-evolving medical data. This complexity and size becomes an obstacle to integration and information processing interoperability. The Open Biomedical Ontologies (OBO) consortium is trying to solve this obstacle by introducing a strategy for ontology evolution. Biomedical ontologies include overlapping information. This overlapping information is necessary for the integration and information processing interoperability of biomedical systems. A relation between different candidate ontologies is referred to as ontology mapping or alignment. The candidate ontologies for mapping procedure of establishing the relation, and the procedure for finding the mapping to establish the relation between different ontologies is referred to as ontology matching.
The ontology matching procedure for finding the ontology mapping on the large-scale biomedical ontology requires two-phase operational complexity and excessive operation jobs. Referring to “On Matching Large Life Science Ontologies in Parallel” published in Data Integration in the Life Sciences (2010), the ontology matching is calculated by the Cartesian product between two candidate ontologies. This job requires a resource-based matching algorithm. The excessive operation jobs due to the two-phase operational complexity in the mapping procedure may cause a delay, and the delay of the mapping result causes the ontology mapping for the biomedical system to ineffectively perform the processing within a processing due time.